Algorithms for scene rotation
If deep learning is what you are trying to use here, you should keep in mind that the real intent behind deep learning is to learn a probability distribution, which means that if you were to use a deep learning model to &quot;rotate&quot; images, you can only do it on a specific class of images (e.g. faces, cats, etc...).
If that's your goal, generative models are the way to go:
Autoencoders
You can train an Autoencoders to slightly change the angle. Autoencoders are a special type of neural network that is trained to output the same input you feed into them with a few imposed restrictions to prevent it from learning a trivial identity function. In your case, you could use a variation of a de-noising autoencoder. A de-noising autoencoder, as the name suggests, generates the same input image minus an artificial stochastic noise. The way this is achieved is by feeding a corrupted version of the image and then evaluating the loss on the non-corrupted version.
How can this be adapted in your case?
In your case, you could feed the original images into your autoencoder and evaluate them based on the rotated images. This will result in your autoencoder effectively learning the inner distribution that generates the images in order to generate a slightly &quot;rotated&quot; version of it. For more info on de-noising autoencoders, see the original paper.
Generative adversarial networks
For a more sophisticated approach, you can use generative adversarial networks. GANs are relatively harder to manipulate, but usually perform better than other generative models when it comes to images.
How can this be adapted in your case?
In general, GANs generate images from noise. However, in your case, you can use the original (non rotated) images as input for the generator. The generator can be a convolutional autoencoder for example. And the &quot;real images&quot; dataset will be your rotated images. This way, your model will learn to generate slightly rotated images by being fed an equivalent of noise in traditional GANs which in your case will be the original images. For more info on GANs, I suggest this and the original paper.
I should point out that one of the flaws of GANs is distorted perspectives so it's probably a shot in the dark; however, I think it won't be a problem here because you would be using real images as input instead of complete noise.
Related work
Now, as far as the literature goes, I don't think this has been done before except for this one (kinda) for faces representation learning. The only way this is similar to yours is that you can modify the implementation to only generate the faces it has been fed from different perspectives instead of an average of everything it has learned.